Ring-flash-2.0 API, Deployment, Pricing

Qwen2.5-72B-Instruct is one of the latest large language model series released by Alibaba Cloud. The 72B model demonstrates significant improvements in areas such as coding and mathematics. The model also offers multilingual support, covering over 29 languages, including Chinese and English. It shows notable enhancements in following instructions, understanding structured data, and generating structured outputs, particularly in JSON format....

Qwen2.5-7B-Instruct is one of the latest large language model series released by Alibaba Cloud. This 7B model demonstrates significant improvements in areas such as coding and mathematics. The model also offers multilingual support, covering over 29 languages, including Chinese, English, and others. The model shows notable enhancements in instruction following, understanding structured data, and generating structured outputs, particularly JSON....

Qwen2.5-14B-Instruct is one of the latest large language model series released by Alibaba Cloud. This 14B model demonstrates significant improvements in areas such as coding and mathematics. The model also offers multi-language support, covering over 29 languages, including Chinese and English. It has shown notable advancements in instruction following, understanding structured data, and generating structured outputs, particularly in JSON format....

Qwen2.5-32B-Instruct is one of the latest large language models series released by Alibaba Cloud. This 32B model demonstrates significant improvements in areas such as coding and mathematics. The model also offers multi-language support, covering over 29 languages, including Chinese, English, and others. It shows notable enhancements in instruction following, understanding structured data, and generating structured outputs, particularly in JSON format....

Qwen2.5-72B-Instruct is one of the latest large language models series released by Alibaba Cloud. This 72B model demonstrates significant improvements in areas such as coding and mathematics. It supports a context length of up to 128K tokens. The model also offers multilingual support, covering over 29 languages, including Chinese, English, and others. It has shown notable enhancements in instruction following, understanding structured data, and generating structured outputs, particularly in JSON format....

Qwen2.5-Coder-32B-Instruct is a code-specific large language model developed based on Qwen2.5. The model has undergone training on 5.5 trillion tokens, achieving significant improvements in code generation, code reasoning, and code repair. It is currently the most advanced open-source code language model, with coding capabilities comparable to GPT-4. Not only has the model enhanced coding abilities, but it also maintains strengths in mathematics and general capabilities, and supports long text processing....

DeepSeek-VL2 is a mixed-expert (MoE) vision-language model developed based on DeepSeekMoE-27B, employing a sparse-activated MoE architecture to achieve superior performance with only 4.5B active parameters. The model excels in various tasks including visual question answering, optical character recognition, document/table/chart understanding, and visual grounding. Compared to existing open-source dense models and MoE-based models, it demonstrates competitive or state-of-the-art performance using the same or fewer active parameters....

Qwen2.5-VL is a vision-language model in the Qwen2.5 series that shows significant enhancements in several aspects: it has strong visual understanding capabilities, recognizing common objects while analyzing texts, charts, and layouts in images; it functions as a visual agent capable of reasoning and dynamically directing tools; it can comprehend videos over 1 hour long and capture key events; it accurately localizes objects in images by generating bounding boxes or points; and it supports structured outputs for scanned data like invoices and forms. The model demonstrates excellent performance across various benchmarks including image, video, and agent tasks...

The new version of DeepSeek-V3 (DeepSeek-V3-0324) utilizes the same base model as the previous DeepSeek-V3-1226, with improvements made only to the post-training methods. The new V3 model incorporates reinforcement learning techniques from the training process of the DeepSeek-R1 model, significantly enhancing its performance on reasoning tasks. It has achieved scores surpassing GPT-4.5 on evaluation sets related to mathematics and coding. Additionally, the model has seen notable improvements in tool invocation, role-playing, and casual conversation capabilities....

DeepSeek-R1-0528 is a reasoning model powered by reinforcement learning (RL) that addresses the issues of repetition and readability. Prior to RL, DeepSeek-R1 incorporated cold-start data to further optimize its reasoning performance. It achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks, and through carefully designed training methods, it has enhanced overall effectiveness...

DeepSeek-R1-Distill-Qwen-14B is a distilled model based on Qwen2.5-14B. The model was fine-tuned using 800k curated samples generated by DeepSeek-R1 and demonstrates strong reasoning capabilities. It achieved impressive results across various benchmarks, including 93.9% accuracy on MATH-500, 69.7% pass rate on AIME 2024, and a rating of 1481 on CodeForces, showcasing its powerful abilities in mathematics and programming tasks...

DeepSeek-R1-Distill-Qwen-32B is a distilled model based on Qwen2.5-32B. The model was fine-tuned using 800k curated samples generated by DeepSeek-R1 and demonstrates exceptional performance across mathematics, programming, and reasoning tasks. It achieved impressive results in various benchmarks including AIME 2024, MATH-500, and GPQA Diamond, with a notable 94.3% accuracy on MATH-500, showcasing its strong mathematical reasoning capabilities...